search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure. More...

#include <pcl/search/kdtree.h>

Inheritance diagram for pcl::search::KdTree< PointT >:

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Collaboration diagram for pcl::search::KdTree< PointT >:

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List of all members.

Public Types
typedef boost::shared_ptr < KdTree< PointT > >	Ptr
typedef boost::shared_ptr < const KdTree< PointT > >	ConstPtr
typedef boost::shared_ptr < pcl::KdTreeFLANN< PointT > >	KdTreeFLANNPtr
typedef boost::shared_ptr < const pcl::KdTreeFLANN < PointT > >	KdTreeFLANNConstPtr
typedef PointCloud::Ptr	PointCloudPtr
Public Member Functions
	KdTree (bool sorted=true)
	Constructor for KdTree.
virtual	~KdTree ()
	Destructor for KdTree.
void	setEpsilon (double eps)
	Set the search epsilon precision (error bound) for nearest neighbors searches.
double	getEpsilon ()
	Get the search epsilon precision (error bound) for nearest neighbors searches.
void	setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices)
	Provide a pointer to the input dataset.
void	setInputCloud (const PointCloudConstPtr &cloud)
	Provide a pointer to the input dataset.
PointCloudConstPtr	getInputCloud ()
	Get a pointer to the input dataset as passed by the user.
virtual IndicesConstPtr const	getIndices ()
	Get a pointer to the set of input indices used as passed by the user.
int	nearestKSearch (const PointT &point, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances)
	Search for the k-nearest neighbors for the given query point.
int	nearestKSearch (const PointCloud &cloud, int index, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances)
	Search for the k-nearest neighbors for the given query point.
int	nearestKSearch (int index, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances)
	Search for the k-nearest neighbors for the given query point (zero-copy).
int	radiusSearch (const PointT &point, const double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, int max_nn=-1) const
	Search for all the nearest neighbors of the query point in a given radius.
int	radiusSearch (const PointCloud &cloud, int index, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, int max_nn=-1)
	Search for all the nearest neighbors of the query point in a given radius.
int	radiusSearch (int index, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, int max_nn=-1) const
	Search for all the nearest neighbors of the query point in a given radius (zero-copy).
virtual void	nearestKSearch (const PointCloud &cloud, const std::vector< int > &indices, int k, std::vector< std::vector< int > > &k_indices, std::vector< std::vector< float > > &k_sqr_distances)
	Search for the k-nearest neighbors for the given query point.
template<typename PointTDiff >
int	nearestKSearchT (const PointTDiff &point, int k, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances)
	Search for the k-nearest neighbors for the given query point.
template<typename PointTDiff >
void	nearestKSearchT (const pcl::PointCloud< PointTDiff > &cloud, const std::vector< int > &indices, int k, std::vector< std::vector< int > > &k_indices, std::vector< std::vector< float > > &k_sqr_distances)
	Search for the k-nearest neighbors for the given query point.
virtual void	radiusSearch (const PointCloud &cloud, const std::vector< int > &indices, double radius, std::vector< std::vector< int > > &k_indices, std::vector< std::vector< float > > &k_sqr_distances, int max_nn=-1)
	Search for all the nearest neighbors of the query point in a given radius.
template<typename PointTDiff >
int	radiusSearchT (const PointTDiff &point, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, int max_nn=-1)
	Search for all the nearest neighbors of the query point in a given radius.
template<typename PointTDiff >
void	radiusSearchT (const pcl::PointCloud< PointTDiff > &cloud, const std::vector< int > &indices, double radius, std::vector< std::vector< int > > &k_indices, std::vector< std::vector< float > > &k_sqr_distances, int max_nn=-1)
	Search for all the nearest neighbors of the query points in a given radius.

Detailed Description

template<typename PointT>
class pcl::search::KdTree< PointT >

search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure.

KdTree is a generic type of 3D spatial locator using kD-tree structures. The class is making use of the FLANN (Fast Library for Approximate Nearest Neighbor) project by Marius Muja and David Lowe.

Author:: Radu B. Rusu

Definition at line 59 of file kdtree.h.

Member Typedef Documentation

template<typename PointT>

typedef boost::shared_ptr<const KdTree<PointT> > pcl::search::KdTree< PointT >::ConstPtr

Reimplemented from pcl::search::Search< PointT >.

Definition at line 69 of file kdtree.h.

template<typename PointT>

typedef boost::shared_ptr<const pcl::KdTreeFLANN<PointT> > pcl::search::KdTree< PointT >::KdTreeFLANNConstPtr

Definition at line 72 of file kdtree.h.

template<typename PointT>

typedef boost::shared_ptr<pcl::KdTreeFLANN<PointT> > pcl::search::KdTree< PointT >::KdTreeFLANNPtr

Definition at line 71 of file kdtree.h.

template<typename PointT>

typedef PointCloud::Ptr pcl::search::Search< PointT >::PointCloudPtr [inherited]

Reimplemented in pcl::search::Octree< PointT, LeafTWrap, OctreeT >, pcl::search::OrganizedNeighbor< PointT >, and pcl::search::AutotunedSearch< PointT >.

Definition at line 76 of file search.h.

template<typename PointT>

typedef boost::shared_ptr<KdTree<PointT> > pcl::search::KdTree< PointT >::Ptr

Reimplemented from pcl::search::Search< PointT >.

Definition at line 68 of file kdtree.h.

Constructor & Destructor Documentation

template<typename PointT>

pcl::search::KdTree< PointT >::KdTree ( bool sorted = true ) [inline]

Constructor for KdTree.

Parameters:

sorted set to true if the nearest neighbor search results need to be sorted in ascending order based on their distance to the query point

Definition at line 81 of file kdtree.h.

template<typename PointT>

virtual pcl::search::KdTree< PointT >::~KdTree ( ) [inline, virtual]

Destructor for KdTree.

Definition at line 88 of file kdtree.h.

Member Function Documentation

template<typename PointT>

double pcl::search::KdTree< PointT >::getEpsilon ( ) [inline]

Get the search epsilon precision (error bound) for nearest neighbors searches.

Definition at line 103 of file kdtree.h.

template<typename PointT>

virtual IndicesConstPtr const pcl::search::KdTree< PointT >::getIndices ( ) [inline, virtual]

Get a pointer to the set of input indices used as passed by the user.

Reimplemented from pcl::search::Search< PointT >.

Definition at line 137 of file kdtree.h.

template<typename PointT>

PointCloudConstPtr pcl::search::KdTree< PointT >::getInputCloud ( ) [inline, virtual]

Get a pointer to the input dataset as passed by the user.

Implements pcl::search::Search< PointT >.

Definition at line 130 of file kdtree.h.

template<typename PointT>

int pcl::search::KdTree< PointT >::nearestKSearch	(	const PointT &	point,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		`[inline, virtual]`

Search for the k-nearest neighbors for the given query point.

Parameters:

[in]	point	the given query point
[in]	k	the number of neighbors to search for
[out]	k_indices	the resultant indices of the neighboring points (must be resized to k a priori!)
[out]	k_sqr_distances	the resultant squared distances to the neighboring points (must be resized to k a priori!)

Returns:: number of neighbors found

Implements pcl::search::Search< PointT >.

Definition at line 151 of file kdtree.h.

template<typename PointT>

int pcl::search::KdTree< PointT >::nearestKSearch	(	const PointCloud &	cloud,
		int	index,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		`[inline, virtual]`

Search for the k-nearest neighbors for the given query point.

Parameters:

[in]	cloud	the point cloud data
[in]	index	the index in cloud representing the query point
[in]	k	the number of neighbors to search for
[out]	k_indices	the resultant indices of the neighboring points (must be resized to k a priori!)
[out]	k_sqr_distances	the resultant squared distances to the neighboring points (must be resized to k a priori!)

Returns:: number of neighbors found

Implements pcl::search::Search< PointT >.

Definition at line 166 of file kdtree.h.

template<typename PointT>

int pcl::search::KdTree< PointT >::nearestKSearch	(	int	index,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		`[inline, virtual]`

Search for the k-nearest neighbors for the given query point (zero-copy).

Parameters:

[in]	index	the index representing the query point in the dataset given by setInputCloud if indices were given in setInputCloud, index will be the position in the indices vector
[in]	k	the number of neighbors to search for
[out]	k_indices	the resultant indices of the neighboring points (must be resized to k a priori!)
[out]	k_sqr_distances	the resultant squared distances to the neighboring points (must be resized to k a priori!)

Returns:: number of neighbors found

Implements pcl::search::Search< PointT >.

Definition at line 184 of file kdtree.h.

template<typename PointT>

virtual void pcl::search::Search< PointT >::nearestKSearch	(	const PointCloud &	cloud,
		const std::vector< int > &	indices,
		int	k,
		std::vector< std::vector< int > > &	k_indices,
		std::vector< std::vector< float > > &	k_sqr_distances
	)		`[inline, virtual, inherited]`

Search for the k-nearest neighbors for the given query point.

Parameters:

[in]	cloud	the point cloud data
[in]	indices	a vector of point cloud indices to query for nearest neighbors
[in]	k	the number of neighbors to search for
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i

Definition at line 184 of file search.h.

template<typename PointT>

template<typename PointTDiff >

int pcl::search::Search< PointT >::nearestKSearchT	(	const PointTDiff &	point,
		int	k,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances
	)		`[inline, inherited]`

Search for the k-nearest neighbors for the given query point.

Use this method if the query points are of a different type than the points in the data set (e.g. PointXYZRGB instead of PointXYZ).

Parameters:

[in]	point	the given query point
[in]	k	the number of neighbors to search for
[out]	k_indices	the resultant indices of the neighboring points (must be resized to k a priori!)
[out]	k_sqr_distances	the resultant squared distances to the neighboring points (must be resized to k a priori!)

Returns:: number of neighbors found

Definition at line 138 of file search.h.

template<typename PointT>

template<typename PointTDiff >

void pcl::search::Search< PointT >::nearestKSearchT	(	const pcl::PointCloud< PointTDiff > &	cloud,
		const std::vector< int > &	indices,
		int	k,
		std::vector< std::vector< int > > &	k_indices,
		std::vector< std::vector< float > > &	k_sqr_distances
	)		`[inline, inherited]`

Search for the k-nearest neighbors for the given query point.

Use this method if the query points are of a different type than the points in the data set (e.g. PointXYZRGB instead of PointXYZ).

Parameters:

[in]	cloud	the point cloud data
[in]	indices	a vector of point cloud indices to query for nearest neighbors
[in]	k	the number of neighbors to search for
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i

Note:: This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.

Definition at line 212 of file search.h.

template<typename PointT>

int pcl::search::KdTree< PointT >::radiusSearch	(	const PointT &	point,
		const double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		int	max_nn = `-1`
	)		const `[inline, virtual]`

Search for all the nearest neighbors of the query point in a given radius.

Parameters:

[in]	point	the given query point
[in]	radius	the radius of the sphere bounding all of p_q's neighbors
[out]	k_indices	the resultant indices of the neighboring points
[out]	k_sqr_distances	the resultant squared distances to the neighboring points
[in]	max_nn	if given, bounds the maximum returned neighbors to this value

Returns:: number of neighbors found in radius

Implements pcl::search::Search< PointT >.

Definition at line 198 of file kdtree.h.

template<typename PointT>

int pcl::search::KdTree< PointT >::radiusSearch	(	const PointCloud &	cloud,
		int	index,
		double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		int	max_nn = `-1`
	)		`[inline, virtual]`

Search for all the nearest neighbors of the query point in a given radius.

Parameters:

[in]	cloud	the point cloud data
[in]	index	the index in cloud representing the query point
[in]	radius	the radius of the sphere bounding all of p_q's neighbors
[out]	k_indices	the resultant indices of the neighboring points
[out]	k_sqr_distances	the resultant squared distances to the neighboring points
[in]	max_nn	if given, bounds the maximum returned neighbors to this value

Returns:: number of neighbors found in radius

Implements pcl::search::Search< PointT >.

Definition at line 215 of file kdtree.h.

template<typename PointT>

int pcl::search::KdTree< PointT >::radiusSearch	(	int	index,
		double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		int	max_nn = `-1`
	)		const `[inline, virtual]`

Search for all the nearest neighbors of the query point in a given radius (zero-copy).

Parameters:

[in]	index	the index representing the query point in the dataset given by setInputCloud if indices were given in setInputCloud, index will be the position in the indices vector
[in]	radius	the radius of the sphere bounding all of p_q's neighbors
[out]	k_indices	the resultant indices of the neighboring points
[out]	k_sqr_distances	the resultant squared distances to the neighboring points
[in]	max_nn	if given, bounds the maximum returned neighbors to this value

Returns:: number of neighbors found in radius

Implements pcl::search::Search< PointT >.

Definition at line 231 of file kdtree.h.

template<typename PointT>

virtual void pcl::search::Search< PointT >::radiusSearch	(	const PointCloud &	cloud,
		const std::vector< int > &	indices,
		double	radius,
		std::vector< std::vector< int > > &	k_indices,
		std::vector< std::vector< float > > &	k_sqr_distances,
		int	max_nn = `-1`
	)		`[inline, virtual, inherited]`

Search for all the nearest neighbors of the query point in a given radius.

Parameters:

[in]	cloud	the point cloud data
[in]	indices	the indices in cloud. If indices is empty, neighbors will be searched for all points.
[in]	radius	the radius of the sphere bounding all of p_q's neighbors
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
[in]	max_nn	if given, bounds the maximum returned neighbors to this value

Definition at line 312 of file search.h.

template<typename PointT>

template<typename PointTDiff >

int pcl::search::Search< PointT >::radiusSearchT	(	const PointTDiff &	point,
		double	radius,
		std::vector< int > &	k_indices,
		std::vector< float > &	k_sqr_distances,
		int	max_nn = `-1`
	)		`[inline, inherited]`

Search for all the nearest neighbors of the query point in a given radius.

Use this method if the query points are of a different type than the points in the data set (e.g. PointXYZRGB instead of PointXYZ).

Parameters:

[in]	point	the given query point
[in]	radius	the radius of the sphere bounding all of p_q's neighbors
[out]	k_indices	the resultant indices of the neighboring points
[out]	k_sqr_distances	the resultant squared distances to the neighboring points
[in]	max_nn	if given, bounds the maximum returned neighbors to this value

Returns:: number of neighbors found in radius

Definition at line 264 of file search.h.

template<typename PointT>

template<typename PointTDiff >

void pcl::search::Search< PointT >::radiusSearchT	(	const pcl::PointCloud< PointTDiff > &	cloud,
		const std::vector< int > &	indices,
		double	radius,
		std::vector< std::vector< int > > &	k_indices,
		std::vector< std::vector< float > > &	k_sqr_distances,
		int	max_nn = `-1`
	)		`[inline, inherited]`

Search for all the nearest neighbors of the query points in a given radius.

Parameters:

[in]	cloud	the point cloud data
[in]	indices	a vector of point cloud indices to query for nearest neighbors
[in]	radius	the radius of the sphere bounding all of p_q's neighbors
[out]	k_indices	the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
[out]	k_sqr_distances	the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
[in]	max_nn	if given, bounds the maximum returned neighbors per query point to this value

Note:: This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.

Definition at line 342 of file search.h.

template<typename PointT>

void pcl::search::KdTree< PointT >::setEpsilon ( double eps ) [inline]

Set the search epsilon precision (error bound) for nearest neighbors searches.

Parameters:

eps	precision (error bound) for nearest neighbors searches

Definition at line 96 of file kdtree.h.

template<typename PointT>

void pcl::search::KdTree< PointT >::setInputCloud	(	const PointCloudConstPtr &	cloud,
		const IndicesConstPtr &	indices
	)		`[inline, virtual]`

Provide a pointer to the input dataset.

Parameters:

cloud	the const boost shared pointer to a PointCloud message
indices	the point indices subset that is to be used from cloud

Reimplemented from pcl::search::Search< PointT >.

Definition at line 113 of file kdtree.h.

template<typename PointT>

void pcl::search::KdTree< PointT >::setInputCloud ( const PointCloudConstPtr & cloud ) [inline, virtual]

Provide a pointer to the input dataset.

Parameters:

cloud the const boost shared pointer to a PointCloud message

Implements pcl::search::Search< PointT >.

Definition at line 122 of file kdtree.h.

The documentation for this class was generated from the following file:

/builddir/build/BUILD/PCL-1.4.0-Source/search/include/pcl/search/kdtree.h

Public Types

Public Member Functions

Detailed Description

template<typename PointT> class pcl::search::KdTree< PointT >

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

template<typename PointT>
class pcl::search::KdTree< PointT >